@inproceedings{hopp-etal-2025-persistent,
    title = "Persistent Homology of Topic Networks for the Prediction of Reader Curiosity",
    author = "Hopp, Manuel D.s.  and
      Labatut, Vincent  and
      Amalvy, Arthur  and
      Dufour, Richard  and
      Stone, Hannah  and
      Jach, Hayley K  and
      Murayama, Kou",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1364/",
    pages = "28121--28132",
    ISBN = "979-8-89176-251-0",
    abstract = "Reader curiosity, the drive to seek information, is crucial for textual engagement, yet remains relatively underexplored in NLP. Building on Loewenstein{'}s Information Gap Theory, we introduce a framework that models reader curiosity by quantifying semantic information gaps within a text{'}s semantic structure. Our approach leverages BERTopic-inspired topic modeling and persistent homology to analyze the evolving topology (connected components, cycles, voids) of a dynamic semantic network derived from text segments, treating these features as proxies for information gaps. To empirically evaluate this pipeline, we collect reader curiosity ratings from participants (*n* = 49) as they read S. Collins{'}s ``The Hunger Games'' novel. We then use the topological features from our pipeline as independent variables to predict these ratings, and experimentally show that they significantly improve curiosity prediction compared to a baseline model (73{\%} vs. 30{\%} explained deviance), validating our approach. This pipeline offers a new computational method for analyzing text structure and its relation to reader engagement."
}